Oral presentations and abstracts

Over the past decade there has been a large increase in the number of automated camera networks that monitor the sky for fireballs. One of the goals of these networks is to provide the necessary information for linking meteorites to their pre-impact, heliocentric orbits and ultimately to their source regions in the solar system. We re-computed heliocentric orbits for the 25 meteorite falls published in or before 2016 from original data sources (Granvik and Brown 2018). Using these orbits, we constrained their most likely escape routes from the main asteroid belt and the cometary region by utilizing a state-of-the-art orbit model of the near-Earth-object population (Granvik et al. 2016), which includes a size-dependence in delivery efficiency. While we find that the general results for escape routes are comparable to previous work, the role of trajectory measurement uncertainty in escape-route identification is explored for the first time. Moreover, the improved size-dependent delivery model substantially changes likely escape routes for several meteorite falls, most notably Tagish Lake which seems unlikely to have originated in the outer main belt as previously suggested. In addition, we find that reducing the uncertainty of fireball velocity measurements below about 0.1 km/s does not lead to reduced uncertainties in the identification of their escape routes from the asteroid belt and, further, their ultimate source regions. The analysis suggests that camera networks should be optimized for the largest possible number of meteorite recoveries with measured speed precisions of order 0.1 km/s. We will present updated results based on a new NEO model (Granvik et al. 2018) and complement our data set with the falls that have been reported since 2016.

References:
Granvik, M. and Brown, P. (2018). "Identification of meteorite source regions in the Solar System", Icarus 311, 271-287.
Granvik, M., Morbidelli, A., Jedicke, R., Bolin, B., Bottke, W. F., Beshore, E., Vokrouhlicky, D., Delbo, M., Michel, P. (2016). "Super-catastrophic disruption of asteroids at small perihelion distances", Nature 530, 303-306.
Granvik, M., Morbidelli, A., Jedicke, R., Bolin, B., Bottke, W. F., Beshore, E., Vokrouhlicky, D., Nesvorny, D., Michel, P. (2018). "Debiased orbit and absolute-magnitude distributions for near-Earth objects", Icarus 312, 181-207.

How to cite: Granvik, M. and Brown, P.: Source regions for meteorite falls, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-774, https://doi.org/10.5194/epsc2020-774, 2020.

Cometary meteoroid trails exist in the vicinity of comets, forming fine structure of the interplanetary dust cloud. The trails consist predominantly of the largest cometary particles (with sizes of approximately 0.1 mm to 1 cm) which are ejected at low speeds and remain very close to the comet orbit for several revolutions around the Sun. In the 1970s two Helios spacecraft were launched towards the inner solar system. The spacecraft were equipped with in-situ dust sensors which measured the distribution of interplanetary dust in the inner solar system for the first time. 

When re-analysing the Helios data, Altobelli et al. (Astron. Astrophys., 448, 243-252, 2006) recognized a clustering of seven impacts, detected by Helios in a very narrow region of space at a true anomaly angle of 135 +/- 1 degrees, which the authors considered as potential cometary meteoroid trail particles. At the time, however, this hypothesis could not be studied further.

We re-analyse these candidate cometary trail particles in the Helios dust data to investigate the possibility that some or all of them indeed  originate from cometary trails and we constrain their source comets.

The Interplanetary Meteoroid Environment for eXploration (IMEX) dust streams in space model is a new universal model for cometary meteoroid streams in the inner solar system, developed by Soja et al. (Astron. Astrophys., 583, A18, 2015). We use IMEX to study cometary trail traverses by  Helios. 

During ten revolutions around the Sun, the Helios spacecraft intersected 13 cometary meteoroid trails. For the majority of these traverses the predicted dust fluxes are very low. In the narrow region of space where Helios detected the candidate dust particles, however, the spacecraft repeatedly traversed the trails of comets 45P/Honda-Mrkos-Pajdusakova and 72P/Denning-Fujikawa with relatively high predicted dust fluxes. 

The analysis of the detection times and particle impact directions shows that four detected particles are compatible with an origin from these two comets. By combining measurements and simulations we find a dust spatial density in these trails of approximately 10^-8 to 10^-7 m^-3.

The identification of potential cometary meteoroid trail particles in the Helios data greatly benefitted from the clustering of trail traverses in a rather narrow region of space. The in-situ detection and analysis of meteoroid trail particles which can be traced back to their source bodies by spacecraft-based dust analysers opens a new window to remote compositional analysis of comets and asteroids without the necessity to fly a spacecraft to or even land on those celestial bodies. This provides new science opportunities for future space missions like Destiny+, Europa Clipper and IMAP.

How to cite: Krüger, H., Strub, P., Sommer, M., Altobelli, N., Kimura, H., Lohse, A.-K., Grün, E., and Srama, R.: Helios spacecraft data revisited: Detection of cometary meteoroid trails by in-situ dust impacts, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-320, https://doi.org/10.5194/epsc2020-320, 2020.

The dynamical evolution of simulated meteoroid stream of the Quadrantids ejected from the parent body of the asteroid (196256) 2003 EH1 expects possible scenario for resonant motion. We found a peculiar behavior for this stream. Here, we show that the orbits of some ejected particles are strongly affected by the Lidov–Kozai mechanism that protects them from close encounters with Jupiter. Lack of close encounters with Jupiter leads to a rather smooth growth in the parameter MEGNO (Mean Exponential Growth factor of Nearby Orbits) and the behavior imply the stable motion of simulation particles of the Quadrantids meteoroid stream. A rather smooth path with nearly constant semi-major axis is obtained due to lack of close encounters with Jupiter. The coupled oscillation of the three orbital parameters, e, i, and ω, for stable ejected particles is observed.

However, close encounters with Jupiter are not treated by the Kozai formalism and can transfer particles away from the Kozai trajectories for unstable ejected particles over time. Other ejected particles have chaotic motion from simulations of the orbit of meteoroids are not affected by the Lidov – Kozai mechanism. We suppose that the reasons are the frequent close approaches of the ejected particles with Jupiter and they located near mean motion resonance 2:1J with Jupiter. The motion of these objects has considered to be chaotic in a long-time scale, and the close encounters with Jupiter are supposed to be the cause of the faster chaos. Another reason is that a non-resonant state near the mean motion resonance 2:1J has a strong influence on the motion of the Quadrantid meteor stream. This “weak chaos” is largely confined to the true anomaly. Consequently, the shape of the orbit can be computed reliably over much longer time scales than can the body’s position within the orbit. High value of the parameter MEGNO are due to frequent changes in semimajor axis induced by multiple close encounters with Jupiter near Hill sphere. We finally note that the chaotic behavior of the simulation particles of meteor stream may be caused not only by close encounter with planets but also by unstable mean motion or secular resonances.

We conjecture that the reasons of chaos are the overlap of stable secular resonances and unstable mean motions resonances and close and/or multiple close encounters with the major planets. The orbits of some ejected particles are strongly affected by the Lidov–Kozai mechanism that protects them from close encounters with Jupiter that leads to a rather smooth growth in the parameter MEGNO and the behavior imply the stable motion of simulation particles of the Quadrantids meteoroid stream.

The research was carried out within the state assignment of Ministry of Science and Higher Education of the Russian Federation (theme No. 0721-2020-0049)

References

Abedin, A., Spurný, P., Wiegert, P., Pokorný, P., Borovi cka, J., Brown, P., 2015. On the age and formation mechanism of the core of the Quadrantid meteoroid stream. Icarus 261, 100–117.

Cincotta, P.M., Girdano, C.M., Simo, C., 2003. Phase space structure of multi-dimensional systems by means of the mean exponential growth factor of nearby orbits. Phys. Nonlinear Phenom. 182 (3–4), 151–178.

Chirikov, B.V., 1979. A universal instability of many-dimensional oscillator systems. Phys. Rep. 52 (5), 263–379.

Galushina, T.Yu, Sambarov, G.E., 2017. The dynamical evolution and the force model for asteroid (196256) 2003 EH1. Planet. Space Sci. 142, 38.

Galushina, T.Yu, Sambarov, G.E., 2019. Dynamics of asteroid 3200 Phaethon under overlap of different resonances. Sol. Syst. Res. 53 (3), 215–223.

Gonczi, R., Rickman, H., Froeschle, C., 1992. The connection between Comet P/Machholz and the Quadrantid meteor. Mon. Not. Roy. Astron. Soc. 254, 627.

Hughes, D.W., Taylor, I.W., 1977. Observations of overdense Quadrantid radio meteors and the variation of the position of stream maxima with meteor magnitude. Mon. Not. Roy. Astron. Soc. 181, 517.

Kozai, Y., 1962. Secular perturbations of asteroids with high inclination and eccentricity. Astron. J. 67, 591–598.

Lidov, M.L., 1962. The evolution of orbits of artificial satellites of planets under the action of gravitational perturbations of external bodies. Planet. Space Sci. 9, 719.

Williams, I.P., Ryabova, G.O., Baturin, A.P., Chernitsov, A.M., 2004a. The parent of the Quadrantid meteoroid stream and asteroid 2003 EH1. Mon. Not. Roy. Astron. Soc. 355 (4), 1171–1181.

How to cite: Sambarov, G. E., Galushina, T. Yu., and Syusina, O. M.: How does the Lidov–Kozai mechanism protect Quadrantids meteoroid stream from close encounters with Jupiter?, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-551, https://doi.org/10.5194/epsc2020-551, 2020.

We describe a universal meteor simulation tool set named ASMODEUS and present several of its possible use cases. The toolset consists of a Monte-Carlo simulator of meteoroids entering the Earth’s atmosphere, functions for transformation to observer-centred coordinate frames representing virtual views of the sky, application of observational bias effects and a number of statistical tools for analyses of produced data sets and comparison to real-world data. The simulation has already been used in several areas of research, most notably estimates of meteoroid flux and de-biasing of real-world meteor observations and in investigation of how varying the initial properties of meteoroids affects the resulting meteors. It lends itself to many more possible applications, such as assessment of selection bias in ground-based observing systems, investigation of models of meteor flight and ablation, and evaluation of mass and population indices of meteor showers.

How to cite: Baláž, M., Tóth, J., Vereš, P., and Jedicke, R.: ASMODEUS Meteor Simulation Tool, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-341, https://doi.org/10.5194/epsc2020-341, 2020.

Mini-EUSO is a very wide (44°x44°) field of view telescope installed on August 2019 inside the Zvezda Module of the ISS, looking nadir through a UV transparent window and taking data since October 2019. Its optical system consists of two Fresnel lenses, focusing the light onto an array of 36 multi-anode photomultiplier tubes. The focal surface counts a total of 2304 pixels, each one having a footprint of about 6.5 km on ground. The instrument triggers on two different timescales, respectively 2.5 μs (D1) and 320 μs (D2), and perform a continuous monitoring of the UV emission at a 40.96 ms timescale (D3). At time of writing, about one thousand meteors on D3 data have been classified as meteors using our current detection algorithm. We describe here a concept of an alternative algorithm to recognize meteors in the D3 continuous data-stream, which can be also implemented in the future for online triggering, and show some examples of detected meteors by our instrument. We also performed a search of possible coincident detections of Mini-EUSO meteors by ground meteor and fireball networks, such as PRISMA in Italy, to gain a stereoscopic vision of the event itself. In light of these initial results, we present here the capabilities of Mini-EUSO instrument in meteor science.

How to cite: Barghini, D., Battisti, M., Belov, A., Bertaina, M. E., Bisconti, F., Capel, F., Casolino, M., Ebisuzaki, T., Gardiol, D., Klimov, P., Marcelli, L., Miyamoto, H., Picozza, P., Piotrowski, L. W., Prévot, G., Reali, E., Sakaki, N., and Takizawa, Y. and the Mini-EUSO collaboration: Meteor detection from space with Mini-EUSO telescope, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-800, https://doi.org/10.5194/epsc2020-800, 2020.

Near-Earth objects (NEOs) are typically fiercely monitored due to the inherent danger of their close encounters. Encounters with more massive objects at distances of a few lunar distances (LD) are relatively commonplace. However, fireball and meteor observation networks from around the world have witnessed ‘grazing’ events occur on several occasions [1, 2, 3, 4, 5]. Grazing events are characterized by their low impact angle and their possible re-entry into interplanetary space. These fireballs display how there are likely many smaller objects, that cannot be detected telescopically, that encounter the Earth all the time. Close encounters can quickly scatter meteoroids into drastically distinct orbits. This process is exemplified by the grazing fireball event detected by the Desert Fireball Network (DFN) in 2017 [5]. During this event, a ≥ 0.3 m object grazed the atmosphere coming from an Apollo-type orbit and exited with a JFC-like orbit. In order to characterize the population of objects in this small size range, we utilized the data collected by the Desert Fireball Network (DFN). The DFN is a continental-scale photographic fireball monitoring network covering over 2.5 million square kilometers of the Australian outback. The Earth’s close encounter flux in the 0.01-100 kg range was estimated using the impact flux observed by the DFN. To do this, several inherent biases had to be taken into account. Some of these biases include: limiting sensitivity of the fireball observatories, seasonal and diurnal variations in the flux, and gravitational focusing. These biases were all taken into consideration. The size-range analyzed in the DFN dataset was cutoff at small-sizes in order to remove the excess of fast, small meteoroids. Whereas, the diurnal and seasonal effects on the average flux of the DFN were considered negligible [6]. Most importantly, gravitational focusing must be corrected for or the flux of slower asteroidal material would be overestimated. The flux enhancement factor was accounted for using the global average enhancement determined by Opik [7], and scaled accordingly based on close encounter ¨ distance. In total, the close encounter population was modeled using 2.3 million test particles. The close encounter simulations, based on the DFN orbital dataset, demonstrated a significant population of close encounters at the centimeter/meter scale. Most of these bodies are negligibly affected during their close encounters; however, many experience considerable orbital changes (Fig. 1). Since the most likely objects to encounter the Earth are those with orbits more similar to the Earth, many close encounters come from asteroid-like (T_J > 3) objects. During the encounter, objects either gain or lose energy resulting in an inverse change to the objects T_J value. In total there appears to be a net gain of objects flung from asteroidal to JFC-like orbits. These encounters are considerably rare (about 0.16% of the total flux within 1.5 LD); however, considering the vast number of objects predicted to have close encounters at these small sizes, the size of this scattered population is not insignificant.

References: [1] Z Ceplecha. In: Bull. Astron. Inst. Czechoslov. 30 (1979), pp. 349–356. [2] J Borovicka and Z Ceplecha. In: A&A 257 (1992), pp. 323–328. [3] D. O. Revelle, R. W. Whitaker, and W. T. Armstrong. In: vol. 3116. 1997, pp. 156–167. [4] J.M. Madiedo et al. In: MNRAS 460.1 (2016), pp. 917–922. [5] Patrick M Shober et al. “Where Did They Come From, Where Did They Go: Grazing Fireballs”. In: The Astronomical Journal 159.5 (2020), p. 191. [6] I. Halliday and A.A. Griffin. In: Meteoritics 17.1 (1982), pp. 31–46. [7] E.J. Opik. ¨ In: Proc. R. Ir. Acad. 1951, pp. 165–199.

How to cite: Shober, P., Jansen-Sturgeon, T., Devillepoix, H., Sansom, E., Bland, P., Towner, M., Cupak, M., Howie, R., and Hartig, B.: Meteoroids Scattered by the Earth, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-884, https://doi.org/10.5194/epsc2020-884, 2020.

The visual observation of meteors has gathered over the last century the interest of scientists and the fascination of public. As the meteor observational techniques were spread worldwide, the meteor research community was avid of reliable mathematical relationships to derive further clues on these events: their orbital origin, their hazardous potential, etc. For such purpose, the combination of the meteor event observed flight parameters seemed to comply with these goals. Moreover, this approach was little by little more feasible given the technology growth that gradually improved the accuracy of the observations. Amongst all the suggested flight parameter relationships available in the literature, the one introduced by Ceplecha and McCrosky [1] in the mid 70’s became timely and used in many studies as a ‘ground truth’. The empirical work of Ceplecha and McCrosky [1] was also mathematically supported by the single body Newtonian formulation that is still widely used to model the meteor trajectory [2]. Moreover, Ceplecha and McCrosky [1] expanded the use of this relationship to elaborate a meteor classification. The classification relies on the value of a criterion, called PE, which ranks the value of the correlation given by the parameters included in the relationship. Although the authors did not expect the classification to be extremely accurate, it allows quick interpretation of the event under study. Consequently, both the criterion and the classification have played a relevant role in the scientific publication over the decades.

The PE criterion relates the meteor observed end height to its atmospheric entry velocity, mass and flight trajectory angle. To keep the PE calculation straightforward, and because it originally was used for decelerating events, the influence of the meteoroid mass loss (ablation and shape coefficient) was simplified and a mean value for all the meteors registered in the same database was assumed. In the recent years, several alternative formulations for the meteoroid atmospheric flight modelling have been proposed in order to reduce the required analysis-related assumptions and consequent results’ inaccuracies. Amongst them, a formulation based on scaling laws and dimensionless variables has obtained significant results when tackling with different common meteor related studies (see e.g. [3-9]). The main advantage of this methodology is that it provides relevant clues on the event under study removing the necessity of stating initial assumptions on the meteor parameters. Additionally, the accuracy of the outcome is, in most cases, directly linked to the quality of the observations. Interestingly, this new methodology quantifies the meteoroid mass loss in a unique and straightforward way by matching the meteor trajectory (observed height and velocity values) with two dimensionless parameters that are physically meaningful. On one hand, the ballistic coefficient, α, expresses the drag intensity suffered by the meteor body during its flight and it is proportional to the mass of the atmospheric column with the initial meteoroid cross section area along the trajectory divided by the meteoroid’s pre-atmospheric mass. On the other, the mass loss parameter, β, characterizes the mass loss rate of the meteoroid; it can be expressed as the fraction of the kinetic energy per mass unit of the body that is transferred to the body in the form of heat divided by the effective destruction enthalpy.

Since these two parameters comprise all the meteoroid flight variables earlier included in the PE criterion, but avoid artificial assumptions, we have proposed and studied the hypothesis that there should exist a mathematical expression involving  these two parameters which offers an improved classification criterion [2]. In this work, we verify this hypothesis. The results of our study show that: i) under the same original assumptions [1] the derived log(2αβ) which we advocate using leads to the exactly same PE formula obtained by Ceplecha and McCrosky [1]; ii) the newly offered possibility to include the individual event mass-loss effects in the criterion allows an accurate formulation that still remains simple to implement; iii) the improved criterion is scalable – it is suitable for expanding the classification beyond fully disintegrating fireballs to larger impactors, including meteorite-dropping fireballs. We use the Prairie Network meteor observations for comparative analysis, which demonstrates the effectiveness and reliability of the new formulation.

References

[1] Ceplecha Z., McCrosky R. E.,1976, JGR, 81, 6257. https://doi.org/10.1029/JB081i035p06257

[2] Moreno-Ibáñez M., Gritsevich M., Trigo-Rodriguez J. M., Silber E. A., 2020, MNRAS, 494 (1), 316. https://doi.org/10.1093/mnras/staa646

[3] Gritsevich M. I., 2009, Adv. Space Res., 44(3), 323.  http://dx.doi.org/10.1016/j.asr.2009.03.030

[4] Gritsevich M. I., Stulov V. P., Turchak L. I.,2012, Cosmic Res., 50(1), 56. http://dx.doi.org/10.1134/S0010952512010017

[5] Bouquet A., Baratoux D., Vaubaillon J., et al. 2014, PSS, 103, 238. http://dx.doi.org/10.1016/j.pss.2014.09.001

[6] Moreno-Ibáñez M., Gritsevich M., Trigo-Rodríguez J. M., 2015, Icarus, 250, 544. http://dx.doi.org/10.1016/j.icarus.2014.12.027

[7] Trigo-Rodríguez J. M., Lyytinen E., Gritsevich M., et al., 2015, MNRAS,449 (2), 2119. http://dx.doi.org/10.1093/mnras/stv378

[8] Lyytinen E., Gritsevich M., 2016, PSS, 120, 35. http://dx.doi.org/10.1016/j.pss.2015.10.012

[9] Sansom E. K., Gritsevich M., Devillepoix H. A. R., et al., 2019, ApJ, 885 (2). https://doi.org/10.3847/1538-4357/ab4516 

How to cite: Moreno-Ibáñez, M., Gritsevich, M., Trigo-Rodríguez, J. M., Silber, E. A., and Peltoniemi, J.: Classification of fireballs: upgrading the PE criterion, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-64, https://doi.org/10.5194/epsc2020-64, 2020.

1. Introduction

When a meteoroid enters the atmosphere, it experiences aerodynamic drag and dynamic pressure. Shock waves can be generated by the hypersonic flight in the atmosphere, fragmentation/airburst and impact in the ground. The hypersonic projectile motion in the atmosphere causes the formation of a Mach cone [1-3]. The shock waves generated during this hypersonic entry propagate almost perpendicular to the trajectory. Fragmentation of the meteoroid creates shock waves that propagate omnidirectionally [1,2]. In large impact events, bolides and/or crater events, the first wave to arrive at the seismic station is the P wave generated directly under the terminal point of the trajectory [4]. After the P wave, air-coupled Rayleigh wave arrive. Airwaves generated by the Mach cone will arrive later as they travel at the speed of sound [1]. The airwave that originates from the point of the trajectory having the shortest distance to the seismic station arrives first and they show the strongest seismic signals in time series data [1,4]. In fireball events, airwaves are a dominant seismic signature [1,4].

2. Aim and Methodology

In this study, we searched for seismic signals from fireballs that have been observed by the Desert Fireball Network (DFN), over a 6-year observational period (2014-2019). The DFN is the world’s largest fireball camera network, located in the Australian outback and consisting of 52 observatories, covering an area of 3 million km² aimed to detect fireballs, recover meteorites and to calculate their orbits [5,6]. We used processed trajectory data from the DFN [6], with seismic data acquired from the Australian National Seismograph Network (ANSN).

The criteria that determined if a seismic signal in time series data could be confidently classified as a signal coming from a fireball event were that the amplitude of the signals must be similar or lower than previously confirmed seismic signals from fireballs, the signal must be within the calculated arrival times of the airwave (direct or ground-coupled Rayleigh wave), there must not be any earthquake activity at the same time, and there must not be any clear anthropogenic-related noise.

We checked if a seismic station could encounter the planar wavefront from the Mach cone. If the shortest distance to the seismic station is perpendicular to the bright flight trajectory and arrival times for the airwaves fit, signals are classified as originating from the Mach cone. If the shortest distance is not perpendicular to the bright flight trajectory, any seismic signals (if they fit with calculated arrival times), are assumed to come from an omnidirectional source that could be caused by fragmentation along the trajectory.

3. Results

Weak and short seismic signals were found for 24 fireball events out of 995 surveyed within 200 km of a seismic station (corresponding to 2.4%). The observed seismic signals in our dataset correspond to airwaves (either as direct airwaves or ground-coupled Rayleigh waves). We found 13 fireballs for which we suspect the signals to have originated from the Mach cone traverse and for 11 fireballs we detected signals that might originate from an airburst. No surveyed fireballs were detected by more than one seismic station. The total of 18 out of 24 signals showed the highest peak in vertical component. The shortest distance between the bright flight trajectory to the seismic station is about 50 km. Fireballs for which seismic signals have been detected cover the complete range of impact angles.

4. Discussion and Conclusion

The weak and short signals that we see in our data are likely direct airwaves, or ground-coupled Rayleigh waves generated by fireball events. In many cases it is not possible to distinguish whether the signal originated from the direct airwave or ground-coupled Rayleigh wave due to overlapping arrival time windows and background noise. The reason why we see signals of some fireballs and not others is probably due to distance, directionality, noise, wind and properties of the seismic station.

We report possible detections of seismic signatures originating from 2.4% of surveyed fireballs observed by the DFN. Unlike other studies who used data from images, seismic stations and infrasound to calculate the orbit and energies of meteors, this study uses information about the trajectory and timing of fireballs observed by the DFN to search for seismic signals.

The importance of this work is evident as these impact events occur on a daily basis, yet are rarely reported as seismic events because their impact energy is often not sufficient to cause quakes that are detectable by seismic stations. Furthermore, understanding frequent meteoroid encounters on Earth could help us make better predictions about what may be impacting Earth and other planetary bodies, such as Mars, in terms of small impact events.

References

[1] Edwards W. N. et al. (2008) Rev. Geophys., 46(4).

[2] Tancredi G. et al. (2009) Meteoritics & Planet. Sci., 44, 1967-1984.

[3] Tauzin B. et al. (2013) Geophys. Res., 40(14), 3522-3526.

[4] Brown P. G. et al. (2003) Meteoritics & Planet. Sci., 38, 989-1003.

[5] Devillepoix H. A. R. et al. (2018) Meteoritics & Planet. Sci., 53(10), 2212-2227.

[6]  Devillepoix H. A. R. et al. (2019) MNRAS, 483(4), 5166-5178.

How to cite: Neidhart, T., Miljković, K., Sansom, E. K., Devillepoix, H. A. R., Kawamura, T., Dimech, J., Wieczorek, M., and Bland, P. A.: Suspected seismic signals from DFN fireballs, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-638, https://doi.org/10.5194/epsc2020-638, 2020.

Main text

The UK has a long history of meteorite falls (where the meteorite fireball is witnessed, and the stone recovered, dating back to 1623 (MetBull, 2020). But the last meteorite fall in the UK was nearly 30 years ago when the Glatton stone, an L6 ordinary chondrite was recovered in 1991 (Hutchinson et al., 1991). Meteorite falls are important samples as they are usually recovered within days of the fireball event.  As such, they have not experienced the deleterious effects terrestrial weathering that can change their extraterrestrial mineralogy, chemistry and isotopic composition (Bland et al., 2006). In exceptional circumstances rapidly recovered falls may have avoided rainfall so that soluble extraterrestrial minerals such as salts may be preserved (Chan et al., 2018). Therefore, meteorite falls represent much more pristine extraterrestrial material than their find counterparts within the same group, and consequently, characterisation of their texture and chemical signatures provides a clearer window into solar system processes. However, even falls are limited in their interpretive power as the geological context of the stone (i.e. where in the solar system it originated from) is unknown.

To derive this contextual information requires the imaging of the fireball event from multiple geographical positions (Devillepoix et al., 2020). This data provides two vital pieces of information; the initial orbit of the meteoroid can be calculated and the final fall position can be predicted with increased accuracy (Devillepoix et al., 2020). As such, dedicated fireball camera networks (Bowden, 2006) are entering ‘a golden age’ with improvements in hardware and software capabilities as well as a reduction in production costs (Spurný et al., 2014). Continent-scale observatories have been established (Howie et al., 2017) and global networks are under construction (Devillepoix et al., 2020). In addition, these same developments have enabled the amateur astronomy community to construct their own networks either as groups or individuals with functional data pipelines and observations that can rival funded networks. Consequently, the number of recovered meteorite falls with orbits globally has grown rapidly over recent years (Borovička et al., 2015).

The UK is a hotbed for such activity but does not have a recent recovered meteorite fall…yet. There are currently two active academically funded networks in the UK: the UK Fireball Network (UKFN: part of the Global Fireball Observatory built on the hardware and software developed by the Desert Fireball Network in Australia), and the System for Capture of Asteroid and Meteorite Paths (SCAMP; the UK arm of the French-led Fireball Recovery and InterPlanetary Observation Network (FRIPON)) (Figure 1, 2). In addition, there are two major amateur networks the UK Meteor Observation Network (UKMON), and NEMETODE, as well as an emerging presence of the Raspberry-Pi based Global Meteor Network and countless individual citizen scientists also imaging the UK night skies (Figure 1, 2).

However, environmental factors in the UK such as light pollution and regular low-lying cloud cover means that a single fireball may not be captured multiple times by one network, preventing them from calculating an orbit and accurate fall position. As such, these networks, together with UK planetary scientists and National museums, have formed a collaborative data-sharing initiative called the UK Fireball Alliance (UKFAll) in order to maximise the chances of capturing a meteorite-dropping fireball event that makes landfall on the UK.

Since the initiation of UKFAll in late 2018 many joint fireball observations have been made between UK networks including a fireball on the 16^th February 2020 that likely dropped a few 10s of grams of extraterrestrial material into the North Sea (Figure 3).

However, the diversity of hardware, software and data processing pipelines for capturing fireball events vary between camera networks. This hinders the speed of detecting and triangulating a meteorite-dropping fireball event as each fireball requires a bespoke solution to translate the data into the other networks’ format. A consistent method for rapidly transferring and converting the diversity of outputs produced by each network into a standard format that can be read and utilised by each network is required and is critical to facilitate a rapid UK response to fireball events and associated recovery effort.

Here we describe the first iteration of a new code that will enable rapid conversion of data outputs from both video and still image camera networks. The code provides an effective bridging solution, while the ultimate aim is to agree and implement a globally accepted standardised format for fireball observations that can be readily transferred and utilised between camera networks to facilitate meteorite fall recovery.

We also describe the logistical issues encountered by UKFAll and the solutions being implemented, including: recruitment of citizen scientists as searchers; conduct and liability issues; and best practice for collection.

References

Bland, P.A., et al., (2006). Weathering of chondritic meteorites. Meteorites and the early solar system II, 1, 853-867.

Borovička J., et al., (2015). Small near‐Earth asteroids as a source of meteorites. In Asteroids IV, edited by Michel P., DeMeo F.E., and Bottke W.F. Tucson, Arizona: University of Arizona Press. pp. 257–280.

Bowden, A.J. 2006. “Meteorite provenance and the asteroid connection”. In The history of meteoritics and key meteorite collections; fireballs, falls and finds, Edited by: G.J.H., McCall, A.J., Bowden and R.J., Howarth. Vol. 256, 379–403. Geological Society, London, Special Publications.

Chan, Q.H., et al., (2018). Organic matter in extraterrestrial water-bearing salt crystals. Science advances, 4(1), eaao3521.

Devillepoix, H.A., et al., (2018). The dingle dell meteorite: a halloween treat from the main belt. Meteoritics & Planetary Science, 53(10), 2212-2227.

Devillepoix, H.A.R., et al., (2020). A Global Fireball Observatory. arXiv preprint arXiv:2004.01069.

Howie, R.M., et al., (2017). How to build a continental scale fireball camera network. Experimental Astronomy, 43(3), 237-266.

Hutchison, R., et al., (1991). The L6 chondrite fall at Glatton, England, 1991 May 5. Meteoritics, 26, 349.

MetBull (2020), The meteoritical bulletin database. URL: https://www.lpi.usra.edu/meteor/metbull.php

Spurný P., et al., (2014). Reanalysis of the Benešov bolide and recovery of polymict breccia meteorites—Old mystery solved after 20 years. Astronomy & Astrophysics 570:A39.

How to cite: Daly, L., McMullan, S., Rowe, J., Collins, G. S., Suttle, M., Chan, Q. H. S., Young, J. S., Shaw, C., Mardon, A. G., Alexander, M., Tate, J., Fireball Network Team, T. D., Campbell-Burns, P., Kacerek, R., King, A., Joy, K., Christou, A., Horák, J., and Shepherd, J.: The UK Fireball Alliance (UKFAll); combining and integrating the diversity of UK camera networks to aim to recover the first UK meteorite fall for 30 years, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-705, https://doi.org/10.5194/epsc2020-705, 2020.

In the UK there are five meteor camera networks using four different camera and software systems that are aiming to recover meteorites. Utilising all observations of a fireball event from each network is crucial to constrain a precise orbit and fall position. However, the various camera systems generate a diversity of data outputs that are not compatible with each other. As a result, when a potentially meteorite-dropping fireball event occurs it is currently challenging to exchange calibrated observations between networks, which creates obstacles to response time and rapid meteorite recovery.

If recorded by at least two observatories, the fireball’s trajectory, pre-arrival orbit, final mass, and (in combination with a ‘dark flight’ model) the final fall position of any surviving meteorite can also be calculated. For this, the minimum useful data set from each camera consists of (a) the location of the observatory, and (b) a set of timed direction vectors representing each point at which the meteor was observed. While the inclusion of additional data are recommended, this is the minimum required dataset for a fireball observation from a single camera, that can be exchanged between cooperating fireball networks to provide for accurate triangulation.

Camera systems currently used in the UK or considered as candidates for adoption as a data exchange standard are:

• UFOAnalyzer. Used by UK Meteor Observation Network and the NEMETODE network, UFOAnalyzer’s “A.XML” file contains the essential and recommended data in XML format.  UFOAnalyzer is widely used by amateurs in the UK, Western Europe, and Japan.  
• Raspberry Meteor System (RMS, or Global Meteor Network). Increasingly deployed in the UK.  Observations are recorded in two files, the “CAL” file containing all essential metadata, and the “FTPDetect” file containing data for all meteors observed in any given night. 
• Desert Fireball Network (DFN), UK Fireball Network, Global Fireball Observatory – generates a single file with all essential and recommended data, written in Astropy ECSV table format.
• FRIPON, SCAMP - produces a file in Pixmet or SExtractor format, but lacking metadata, which needs to be added from a separate list of observatory parameters.
• Cameras for Allsky Meteor Surveillance (CAMS) – similar to RMS. Used in Benelux countries, which have occasional observational overlap with the UK
• Virtual Meteor Observatory (VMO)¹ – an XML single-meteor format used by some German and Polish networks and by the European Space Agency (ESA). Not yet used in the UK.

Three existing fireball data formats (UFOAnalyzer, VMO and DFN) are identified and evaluated as candidates for information exchange between networks. Each is adequate, though the UFOAnalyzer A.XML format would need to be generalised to be unambiguous. Each can be read with standard Python library routines.  Currently it would appear that the DFN format is the easiest to write using standard library routines and the easiest to represent internally as a data structure.  We are working towards a recommendation for the standard format for fireball data exchange.

Agreeing a common data format enables data sharing but does not require it. Whilst the minimum dataset described above can be utilised effectively, additional information to refine the accuracy of the measurement is also highly desirable and should be included. This recommended data set includes additional information regarding the observing system and uncertainties and additional observations of the event observed at each point in time and will be discussed in detail at the meeting.

We have also developed and present a new converter script that can convert UFOAnalyzer and Desert Fireball Network files to any agreed standard format.  Based on a Jupyter notebook kindly provided by Hadrien Devillepoix of the Desert Fireball Network in Australia, the converter was further developed by SCAMP and is now being developed jointly by the authors.

Whilst it is feasible to produce a converter for a single static format such as UFOAnalyzer’s “A.XML”, it is not practicable to write and maintain a reliable converter program for the multiplicity of file formats that exist now or may exist in the future. As such this converter program represents a stopgap while a global fireball data exchange standard is established.

Conclusion and Recommendation

Meteorite recovery is inhibited in countries where multiple incompatible fireball camera systems are used.  Adopting a common file format that could be read by each system would greatly assist in the observation and recovery of new meteorite falls with orbits.  Pending further consultation, our draft recommendation is adoption of the DFN data format as a global standard for fireball data exchange.

References

¹ Ceplecha, Z. (1987) Geometric, Dynamic, Orbital and Photometric Data on Meteoroids From Photographic Fireball Networks. Bulletin of the Astronomical Institutes of Czechoslovakia 38, 222.

² Devillepoix, H. A. R., Cupák, M., Bland, P. A., Sansom, E. K., Towner, M. C., Howie, R. M., ... & Benedix, G. K. (2020). A Global Fireball Observatory. arXiv preprint arXiv:2004.01069.

³ Sansom, E. K., Rutten, M. G., Bland, P. A. (2017). Analyzing Meteoroid Flights Using Particle Filters. The Astronomical Journal 153, 87-96.

⁴ SonotaCo (2009). “A meteor shower catalog based on video observations in 2007-2008”. WGN, Journal of the International Meteor Organization, 37, 55–62.

⁵ Vida, D., Mazur, M. J., Segon, D., Zubovic, D., Kukic, P., Parag, F., & Macon, A. (2018). First results of a Raspberry Pi based meteor camera system. WGN, the Journal of the International Meteor Organisation 46(2), 71-78.

⁶ Bertin, E., Arnouts, S. (1996.) SExtractor: Software for source extraction. Astronomy and Astrophysics Supplement Series 117, 393-404.

⁷ Jenniskens, P., Gural, P. S., Dynneson, L., Grigsby, B. J., Newman, K. E., Borden, M., Koop, M., Holman, D. (2011). CAMS: Cameras for Allsky Meteor Surveillance to establish minor meteor showers. Icarus 216(1), 40-61.

⁸ Barentsen, G., Arlt, R., Koschny, D., … & Zoladek, P. (2010). The VMO file format. I. Reduced camera meteor and orbit data. WGN, the Journal of the International Meteor Organisation 38(1), 10-24.

How to cite: Rowe, J., Daly, L., McMullan, S., Devillepoix, H., Collins, G., Suttle, M., Chan, Q., Young, J., Shaw, C., Mardon, A., Alexander, M., Tate, J., Cupak, M., Campbell-Burns, P., Kacerek, R., Joy, K., Christou, A., Horák, J., Shepherd, J., and Colas, F. and the The UK Fireball Alliance: Using incompatible fireball camera systems to find meteorites – towards a data exchange standard, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-856, https://doi.org/10.5194/epsc2020-856, 2020.

In this talk we report about the recent finding of two meteorite samples in Italy, near Cavezzo (Modena). The meteorite-dropping fireball was observed on the evening of New Year's Day 2020 by eight all-sky cameras of the PRISMA network, partner of FRIPON. The two fragments, weighing 4 g and 52 g respectively, were collected as a result of a dedicated field search and thanks to the involvement of the local population. 

How to cite: Gardiol, D., Barghini, D., Buzzoni, A., Carbognani, A., Di Carlo, M., Di Martino, M., Knapic, C., Londero, E., Pratesi, G., Rasetti, S., Riva, W., Stirpe, G., Valsecchi, G. B., Volpicelli, C. A., and Zorba, S. and the PRISMA Team: A report on the New Year's meteorite found near Cavezzo, Italy, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-290, https://doi.org/10.5194/epsc2020-290, 2020.

Table 2: (Top) Geocentric and heliocentric radiant and velocities. (Bottom) Orbital elements. 

We reconstructed the trajectory of the SPMN160819 event using the bolide peak brightness obtained from satellite data, a video recording from Eivissa SPMN station, and a casual picture of its persistent trail. The event exemplifies the ability of our software to use Earth-observation satellite data and ground based observations for obtaining the trajectory and orbit of a distant bolide. Even when the number of stations was limited, the atmospheric flight and the terminal mass were computed, indicating that the event was produced by a meteorite-dropper candidate. Unfortunately, our results indicate that any surviving meteorite fell into the Mediterranean Sea.

Acknowledgements

The authors acknowledge financial support from the Spanish Ministry (PGC2018-097374-B-I00, PI: JMTR). We also thank to the casual observers of the event for kindly providing their bolide records: Claudio Porcu and Quico Terradelles i Palau.

References

Jenniskens, P. (1998). Planets and Space, 50, pp. 555.
Whipple, F. L. and Jacchia, L. G. (1957) Smithsonian Contrib. to Astrophys. 1, pp. 183.
Trigo-Rodríguez, J. M. et al. (2008). LPI Contribution No., 1391, 39th LPSC, pp. 1692.
Trigo-Rodríguez, J.M. et al. (2005) Earth Moon and Planets 95, pp. 553.
Ceplecha, Z. (1987) Bull. Astron. Institutes of Czechoslovakia, 38, pp. 222.
Hawkes, R. L. (1993). In Meteoroids and their parent bodies. Astron. Inst., Slovak Acad. Sci., Bratislava, pp. 227.
Sansom, E. K., et al. (2019) The Astrophysical Journal 885, pp. 115.
Langbroek, M. (2004) WGN, Journal of the IMO 32, pp. 109.
Consolmagno, S. and Britt, D. T. (1998) Meteorit. & Planet. Sci. 33, 1231–12
Moreno-Ibáñez M. et al. (2020) MNRAS 494, pp. 316.

How to cite: Peña-Asensio, E., Trigo-Rodríguez, J. M., Mas-Sanz, E., and Ribas, J.: SPMN160819 superbolide: reconstructing its atmospheric trajectory by matching ground-based recordings and satellite data, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-459, https://doi.org/10.5194/epsc2020-459, 2020.

Introduction

Meteor phenomena involve a series of complex aspects, from multiphase physics of the meteoroid (melting and evaporation) to non-equilibrium effects within the flow.

The current meteor physics equations (single-body theory), rely on a zero-dimensional method and lack a precise treatment of the particle interaction with the atmosphere from the fluid dynamics point of view.

Moreover, the study of the material response (melting and possible material removal) is often neglected.

Another approach involves detailed computational simulations of the phenomena. Although these simulations are computationally expensive, they provide physical features of the flow that the single body theory cannot.

The complexity of these detailed simulations significantly increases when one tries to couple all physical aspects, where Golub et al. [1], Johnston and Stern [2], Johnston et al. [3], Shuvalov and Artemieva [4], Svettsov et al. [5] show some examples.

This abstract aims to simulate Lost City entry with a quasi-1D approach employing high-fidelity models by coupling a material solver with a flow solver, where the latter includes radiative features.

Finally, we compare the mass loss from the numerical simulations with the dynamic mass derived from the observations of Ceplecha and ReVelle [6].

Methodology

We describe the coupling procedure to study Lost City ablation, which involves three solvers.

Stagnation-line solver: It solves the discretized Navier-Stokes equations employing the Finite-Volume method. It includes an evaporation boundary condition based on the Hertz-Knudsen model. The open-source library Mutation++ [7] provides the necessary thermodynamic, transport, and kinetic closure to the Navier-Stokes equations.

Radiation solver: It solves the Radiative Transport Equation (RTE) using the tangent slab method allowing the computation of radiative fluxes, radiative powers, and the mass production rate due to photochemistry. Chemical and energy source terms due to radiation are included in the Navier-Stokes equations (stagnation-line solver) by following the work of Soucasse et al. [8] and Dias et al. [9].

Material solver: It solves the material phase-change and the removal of the liquid layer by shear forces. The solver takes as boundary conditions from the stagnation-line solver the heat flux, aerodynamic forces, and evaporation rate.  The reader is referred to Dias et al. [10] for a complete description of the material solver.

Finally, we use an implicit approach to couple the material and flow solver [11, 12].

Results & Conclusion

We simulate a trajectory between 60 km and 53 km – with an interval of 1 km from a point to another – with a constant velocity of 14.15 km/s [6].

A fair comparison has been obtained at the first trajectory points between the numerical results and the dynamic mass derived from observations.

Bellow 57 km, the numerical results underestimate the ablation.

The removal of mass due to shear forces is the primary source of ablation above 54 km, whereas the evaporation rate becomes dominant at 53 km. The evaporation rate increases at lower altitudes due to a surface temperature rise owed to the rise of the radiative heat flux at the surface.

The average shear forces, which causes molten layer removal, increase along the trajectory due to an increase of the free-stream pressure. Despite the rise of the aerodynamic forces, the melting mass removal tends to an asymptotic value. This effect is owed to a decrease of the molten thickness caused by a more substantial evaporation rate.

Regarding the in-dept material, one observes a large temperature gradient close to the surface while the core remains cold.

This large temperature gradient at the surface is a combination of the low material thermal diffusivity and the large mass removal.

Due to the coupling between the flow and material, we have observed a much lower evaporation rate than in the results shown in Dias et al. [9]. 

These results support the importance of material/flow coupling for this type of bolide. Moreover, the liquid layer removal is the dominant source of ablation for most of the trajectory. Additional processes might be missing from our analysis, such as the mass removal due to the inertial forces that might explain the small discrepancy between our results and the observations.

References

[1] A. P. Golub, I. B. Kosarev, I. V. Nemchinov, and V. V. Shuvalov. Emission and Ablation of a Large Meteoroid in the Course of Its Motion through the Earth’s Atmosphere. Solar System Research, 30:183, 1996.

[2] C. O. Johnston and E. C. Stern. A model for thermal radiation from the Tunguska airburst. Icarus, 327:48–59, Jul 2019.

[3] C. O. Johnston, E. C. Stern, and L. F. Wheeler. Radiative heating of large meteoroids during atmospheric entry. Icarus, 309:25 – 44, 2018.

[4] V. Shuvalov and N. Artemieva. Numerical modeling of Tunguska like impacts. Planetary and Space Science, 50(2):181 – 192, 2002.

[5] V. V. Svettsov, V. V. Shuvalov, and O. P. Popova. Radiation from a superbolide. Solar System Research, 52(3):195–205, May 2018.

[6] Z. Ceplecha and D. O. ReVelle. Fragmentation model of meteoroid motion, mass loss, and radiation in the atmosphere. Meteoritics & Planetary Science, 40(1):35–54, 2005.

[7] J. B. Scoggins, V. Leroy, G. Bellas-Chatzigeorgis, B. Dias and T. E. Magin, Mutation++:

MUlticomponent Thermodynamic And Transport properties for IONized gases in C++, arXiv:2002.01783v1 [physics.comp-ph] (submitted to SoftwareX), 2020.

[8] L. Soucasse, J. Scoggins, P. Rivière, T. Magin, and A. Soufiani. Flow radiation coupling for atmospheric entries using a hybrid statistical narrow-band model. Journal of Quantitative Spectroscopy and Radiative Transfer, 180:55–69, 2016.

[9] B. Dias, J.B. Scoggins, T. E. Magin, Luminosity calculation of meteoroid entry based on detailed flow simulations in the continuum regime, Astronomy & Astrophysics 635, A184 (2020).

[10] B. Dias, A. Turchi, E. Stern, and T. E. Magin, A model for meteoroid ablation including melting and vaporization, Icarus, 345(2020) 113710.

[11] P. Schrooyen, A. Turchi, K. Hillewaert, P. Chatelain, and T. E. Magin. Two-way coupled simulations of stagnation-point ablation with transient material response. International Journal of Thermal Sciences, 134:639 –652, 2018.

[12] B. Dias, F. Bariselli, A. Turchi, A. Frezzotti, P. Chatelain, and T. E. Magin, Development of a melting model for meteors, AIP Conference Proceedings 1786(1) 160004.

How to cite: Dias, B. and Magin, T.: Radiation and ablation coupling applied to the study of the Lost City bolide., Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-968, https://doi.org/10.5194/epsc2020-968, 2020.

Distinct Na-enhanced and Na-rich meteor spectra have been previously identified by different authors, but the explanation of their origin and interpretation of the corresponding meteoroid composition was lacking. To study this population, we utilized meteor spectra observations of the global AMOS network and high-resolution Echelle spectra of ablating meteorite samples obtained in a high-enthalpy plasma wind tunnel at the IRS facilities in Stuttgart. It was found that most Na-enhanced and Na-rich spectra can be explained by the effect of low meteor speed related to low ablation temperatures and generally do not reflect real meteoroid composition. Spectra obtained by the laboratory experiment simulating low meteor speeds show corresponding Na-rich spectral profiles irrespectively of the meteorite composition. For more clarity in the classification of Na-enhanced and Na-rich meteoroids, we propose new speed-dependent boundaries between the spectral classes. Based on this classification, we reveal real compositional Na enhancement in five cometary meteoroids including two Perseids, an 𝛼-Capricornid, 𝜈-Draconid and a sporadic. The two Na-enhanced Perseids were linked with increased material strength suggesting that the detected increase of volatile content has implications for the meteoroid structure.

How to cite: Matlovič, P., Tóth, J., Kornoš, L., and Loehle, S.: On the sodium enhancement in spectra of slow meteors and the origin of Na-rich meteoroids, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-609, https://doi.org/10.5194/epsc2020-609, 2020.

We present a new numerical model of the Eta-Aquariid and Orionid meteor shower. Through the modelling of millions meteoroids released from comet 1P/Halley, we simulate the characteristics of each Eta-Aquariid and Orionid apparition between 1985 and 2050. The modelled showers activity duration, shape, maximum zenithal hourly rates (ZHR) values, and mass distributions are compared with several decades of meteor observations in the optical and radar range. Our simulations suggest that the age of the Eta-Aquariids shortly exceeds 5000 years, while the Orionids are composed of older material. Several Eta-Aquariid outbursts are expected in the future, in particular around 2023-2024 and 2045-2046. The evolution of 1P/Halley's meteoroid streams is strongly influenced by mean motion resonances with Jupiter, that might be responsible of a ~12 year cycle in the Orionids activity variations.

How to cite: Egal, A., Brown, P., Wiegert, P., Campbell-Brown, M., Rendtel, J., and Vida, D.: Modeling the past and future activity of the Halleyids meteor showers, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-443, https://doi.org/10.5194/epsc2020-443, 2020.

Abstract

Fourteen Orionids were observed by the Canadian Automated Meteor Observatory’s (CAMO) mirror tracking system. Their radiants were measured with an average precision of 3' and a possible radiant structure was revealed. Ablation modelling shows that light curves, decelerations, and wakes of the observed Orionids can be well modelled using a similar bulk density to the in-situ measurements of dust ejected by the comet 1P/Halley.

Introduction

The Orionids are an annual meteor shower whose parent body is the comet 1P/Halley. The shower mostly has mm-sized particles, but cm-sized Orionid fireball outbursts have been observed in the past (Spurný & Shrbený, 2007). Many previous studies attempted to characterize the radiant dispersion of fainter Orionids, but in most cases the precision of their observations was likely on the same order as the measured dispersion (Kresák & Porubčan, 1970; Hajduk, 1970). Kresák & Porubčan (1970) measured the dispersion of 0.84° (median offset from the mean radiant), while Spurný & Shrbený (2007) measured a dispersion of the resonant Orionid branch to be only 0.12°. Cm-sized meteoroids that are not very affected by non-gravitational forces and are locked in a resonance are naturally expected to have smaller dispersions, but it is not clear whether the dispersion of smaller non-resonant meteoroids was really resolved by Kresák & Porubčan (1970).

Dynamical models are usually utilized to  predict and understand the activity and evolution of meteoroid streams, including the Orionids (e.g. Sato & Watanabe, 2007; McIntosh & Jones, 1988). The accuracy of such models is dependent on knowing the physical properties of the parent body and the dust it produces, especially the bulk density of the ejected dust. The in-situ investigation of physical properties of dust ejected from 1P/Halley was done by the Vega-2 spacecraft – during its 1986 flyby it measured the dust bulk density of 300 kg/m³ (Krasnopolsky et al., 1988).

In this work we use high-precision measurements of the 2019 Orionids and fit a meteoroid ablation model to them. We successfully fit the light curve and deceleration, and for the first time the wake of the observed meteoroids.

Methods

14 Orionids were observed by high-resolution narrow field CAMO cameras (6 arcseconds per pixel, 3 m/px at 100 km precision). The data was manually calibrated and reduced, and the trajectories were computed using the Monte Carlo meteor trajectory estimation method by Vida et al. (2020).

The observed light curve, high-resolution meteoroid deceleration, and wake were fit using the Borovička et al. (2007) meteoroid ablation model which models meteoroid fragmentation as a continuous release of μm-sized grains.

Results

Radiant structure

The measured CAMO radiant dispersion was compared to radiant measurements by the Cameras for All-sky Meteor Surveillance (CAMS; Jenniskens et al., 2011), and the Global Meteor Network[1] (GMN) cameras with 16mm lenses. The comparison is shown in Fig 1. The CAMS and GMN data were filtered by excluding all trajectories with the convergence angles smaller than 15° and a velocity error higher than 15%. Furthermore, all radiants with radiant errors higher than 30 arc minutes for CAMS, and 5 arc minutes for GMN were excluded from the analysis. The radiant error cutoff reflects the stated errors in the datasets themselves and is chosen so that the 25% most precise radiants are used.

Figure 1: Comparison of CAMO, CAMS, and GMN Orionid radiants. Error bars are shown for the GMN and CAMO data, while for CAMS are on the order of 0.5 deg.

Fig 1. shows that both the CAMO and GMN data sets are small in number, which raises concerns about small number statistics. Nevertheless, they are consistent among themselves and the observed radiant dispersion is an order of magnitude higher than the stated radiant measurement precision. Interestingly, the radiants appear to the organized into two possible distinct groups and have a very low variation of the ecliptic latitude of only ~0.1°. In Fig 2., we show how we attempted to separate the radiants into two groups: one cut by ecliptic latitude at β = -7.5°, and the other cut by Sun-centered ecliptic longitude at λ_g – λ_s = 246°. After the radiant drift correction, the latitude cut does not seem to drastically reduce the radiant dispersion of individual groups below the overall dispersion of ~0.4°. Nevertheless, the cut by the Sun-centered longitude reduced the drift-corrected dispersion of the branch with λ_g – λ_s < 246° to only 0.1°. Although further measurements and dynamical modelling are needed to confirm the existence of the two separate groups, we find strong evidence that we have resolved the radiant structure of the Orionids.

Figure 2: CAMO Orionid radiants color coded by the solar longitude.

Figure 3: Dispersion analysis of the two groups split by the Sun-centred ecliptic longitude.

How to cite: Vida, D., Brown, P., and Campbell-Brown, M.: Physical properties and radiant distribution of the Orionids as observed by the Canadian Automated Meteor Observatory’s mirror tracking system, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-456, https://doi.org/10.5194/epsc2020-456, 2020.

The existence of the pairs and larger groups of the meteors was already investigated in several papers. Recent statistical analyses of radar or optical data on several meteor showers provided rather negative results. Nevertheless, some level of groupings is possible, especially among young streams.

When digitalizing older videotapes obtained during the 2006 Geminid campaign, we found a relatively higher number of meteors that appear in pairs. Therefore we investigated this observational result in more detail.

Statistically, when observing Geminid meteor shower, we could expect 1.4 randomly paired meteors per 1 hour assuming ZHR = 100 and the maximum gap between meteors of 1 second. There were recorded 9 pairs during 3.5 hours of observation. Therefore at least some of them cannot be statistically random pairs. Moreover, a triplet of meteors detected within 1 second is well above the statistical threshold for random appearance.

In this talk, we will provide more detailed analyses of all the cases as well as some possible scenarios of their origin.

How to cite: Koten, P. and Čapek, D.: Meteor pairs and groups in Geminid meteor shower, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-552, https://doi.org/10.5194/epsc2020-552, 2020.

A bright fireball together with eight fainter meteors on parallel atmospheric trajectories was observed within 2 seconds by Koten et al. (2017). We will present a detailed analysis of this case focused on the collisional origin of the cluster.

We first estimate the age of the cluster and ejection velocities assuming influence of solar radiation pressure and taking into account observational uncertainties. Next we consider that the parent meteoroid producing the observed fireball collided with a micrometeoroid and that the eight observed meteors correspond to the largest fragments of the ejected material. We estimate the total ejected mass, distribution of mass and velocity of the impactor, and the probability of such an impact. Finally we discuss how reliable the explanation by the collision is.

How to cite: Čapek, D. and Koten, P.: September epsilon Perseid cluster – collisional origin?, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-608, https://doi.org/10.5194/epsc2020-608, 2020.